Abstract 3128: Using Kmerizer, a germline and somatic genotyper for immune associated complex alleles in GuardantINFINITY, for immunotherapy response prediction using cfDNA
Authors: Sante Gnerre, Jun Zhao, Adrian Bubie, Yvonne Kim, Dustin Ma, Indira Wu, Bojan Losic, Tingting Jiang, Han-Yu Chuang
Journal: Cancer Research (2023)

Abstract Background: HLA germline genotypes and somatic mutations show great promise as emerging biomarkers for immune checkpoint inhibitors (ICIs) and understanding patient prognosis. Multiple studies have shown that HLA homozygosity or loss-of-function somatic mutations negatively correlates ICI response rate. Here we present additional data on the algorithm Kmerizer, designed to perform HLA germline typing and somatic mutation detection from cfDNA input material, and we show how we use these in neoantigen prioritization for patient outcome prediction. Methods: Kmerizer first leverages the high depth coverage of targeted sequencing to rapidly identify germline alleles by matching k-mers from the input reads to the k-mers of known HLA alleles. Careful realignment of reads ontothe called germlines is followed by proprietary somatic variant calling. MHC class1 germline allele calls are combined with patient mutation data to generate in silico TCR binding affinity predictions using net MHC-4.0. These predictions are compared across cohorts to assess how cancer type, TMB, and ICI response vary with the predicted neoantigens and TCR binding affinity. Results: Of nineteen cell lines, twelve plasma samples and eight gDNA samples with confirmed HLA typing information, Kmerizer delivered 100% sensitivity on both MHC-I and II genes, with 99.5% and 98.7% specificity, respectively, based on GuardantINFINITY cfDNA sequencing data. For homozygous/heterozygous status, accuracy of 99.1% on class I and 97.7% on class II genes is achieved. HLA allele prevalence among our development samples is consistent with reference cohorts of similar geographic origin in MHC class I genes. The HLA somatic caller achieves &amp;gt;99.99% specificity per base as computed on 48 normal samples, while achieves &amp;gt;91% sensitivity for somatic events with expected allele frequency (AF) ~ 0.15% (AF range[0.08%,0.26%] for detected events) as evaluated through simulations. Additionally, we generated a total of 2,767 immunogenic (ic50&amp;lt;500nM) class-I somatic neoantigens predictions across 112 samples from cancer patients with germline HLA typing results. We found average patient neoantigen TCR binding affinity was significantly associated with cancer type (χ2=86.08,p&amp;lt;0.0001). Top predicted neoantigen binding affinity across patient HLA types were strongly inversely correlated with patient bTMB(rhospearman=-0.25, p&amp;lt;0.0001). Conclusions: The integration of Kmerizer into GuardantINFINITY enables accurate HLA germline and somatic detection along with neoantigen prediction, offering an enhanced and comprehensive biomarker profiling for ICI outcome prediction. Citation Format: Sante Gnerre, Jun Zhao, Adrian Bubie, Yvonne Kim, Dustin Ma, Indira Wu, Bojan Losic, Tingting Jiang, Han-Yu Chuang. Using Kmerizer, a germline and somatic genotyper for immune associated complex alleles in GuardantINFINITY, for immunotherapy response prediction using cfDNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3128.